153 research outputs found
Adaptive Sliding Mode Contouring Control Design Based on Reference Adjustment and Uncertainty Compensation for Feed Drive Systems
Industrial feed drive systems, particularly, ball-screw and lead-screw feed drives are among the dominating motion components in production and manufacturing industries. They operate around the clock at high speeds for coping with the rising production demands. Adversely, high-speed motions cause mechanical vibrations, high-energy consumption, and insufficient accuracy. Although there are many control strategies in the literature, such as sliding mode and model predictive controls, further research is necessary for precision enhancement and energy saving. This study focused on design of an adaptive sliding mode contouring control based on reference adjustment and uncertainty compensation for feed drive systems. A combined reference adjustment and uncertainty compensator for precision motion of industrial feed drive systems were designed. For feasibility of the approach, simulation using matlab was conducted, and results are compared with those of an adaptive nonlinear sliding model contouring controller. The addition of uncertainty compensator showed a substantial improvement in performance by reducing the average contour error by 85.71% and the maximum contouring error by 78.64% under low speed compared to the adaptive sliding mode contouring controller with reference adjustment. Under high speed, the addition of uncertainty compensator reduced the average and absolute maximum contour errors by 4.48% and 10.13%, respectively. The experimental verification will be done in future.
Keywords: Machine tools, Feed drive systems, contouring control, Uncertainty dynamics, Sliding mode control
Neural Network Contour Error Predictor in CNC Control Systems
Paper presented as poster presentation at MMAR 2016 conference (Międzyzdroje,Poland, 29 Aug.-1 Sept. 2016)This article presents a method for predicting contour error using artificial neural networks. Contour error is defined as the minimum distance between actual position and reference toolpath and is commonly used to measure machining precision of Computerized Numerically Controlled (CNC) machine tools. Offline trained Nonlinear Autoregressive networks with exogenous inputs (NARX) are used to predict following error in each axis. These values and information about toolpath geometry obtained from the interpolator are then used to compute the contour error. The method used for effective off-line training of the dynamic recurrent NARX neural networks is presented. Tests are performed that verify the contour error prediction accuracy using a biaxial CNC machine in a real-time CNC control system. The presented neural network based contour error predictor was used in a predictive feedrate optimization algorithm with constrained contour error
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Reference trajectory modification based on spatial iterative learning for contour control of 2-axis NC systems
Contour error is a main factor that affects the quality of products in numerical control (NC) machining. This paper presents a contour control strategy based on digital curves for high-precision control of computer numerical control (CNC) machines. A contour error estimation algorithm is presented for digital curves based on a geometrical method. The dynamic model of the motion control system is transformed from time domain to space domain because the contour error is dependent on space instead of time. Spatial iterative learning control (sILC) is developed to reduce the contour error, by modifying the reference trajectory in the form of G code. This allows system improvement without interference of low-level controllers so it is applicable to many commercial controllers where interpolators and feed-drive controllers cannot be altered. The effectiveness of this method is verified by experiments on a NC machine, which have shown good performance not only for smooth trajectories but also for large curvature trajectories
Hierarchical control of complex manufacturing processes
The need for changing the control objective during the process has been reported in many systems in manufacturing, robotics, etc. However, not many works have been devoted to systematically investigating the proper strategies for these types of problems. In this dissertation, two approaches to such problems have been suggested for fast varying systems. The first approach, addresses problems where some of the objectives are statically related to the states of the systems. Hierarchical Optimal Control was proposed to simplify the nonlinearity caused by adding the statically related objectives into control problem. The proposed method was implemented for contour-position control of motion systems as well as force-position control of end milling processes. It was shown for a motion control system, when contour tracking is important, the controller can reduce the contour error even when the axial control signals are saturating. Also, for end milling processes it was shown that during machining sharp edges where, excessive cutting forces can cause tool breakage, by using the proposed controller, force can be bounded without sacrificing the position tracking performance. The second approach that was proposed (Hierarchical Model Predictive Control), addressed the problems where all the objectives are dynamically related. In this method neural network approximation methods were used to convert a nonlinear optimization problem into an explicit form which is feasible for real time implementation. This method was implemented for force-velocity control of ram based freeform extrusion fabrication of ceramics. Excellent extrusion results were achieved with the proposed method showing excellent performance for different changes in control objective during the process --Abstract, page iv
Design of cross-coupled CMAC for contour-following – a reinforcement-based ILC approach
One of the most popular applications of a bi-axial motion stage is precision motion control. The reduction of tracking error and contour error is one of the most coveted goals in precision motion control systems. The accuracy of a motion control system is often affected by external disturbances. In addition, system non-linearity such as friction also represents a major hurdle to motion precision. In order to deal with the aforementioned problem, this paper proposes a fuzzy logic-based Reinforcement Iterative Learning Control (RILC) and a Cross-Coupled
Cerebellar Model Articulation Controller (CCCMAC). In particular, the proposed fuzzy logicbased RILC and a LuGre friction model-based compensation approach are exploited to improve motion accuracy. The fuzzy logic-based RILC aims at reducing tracking error and compensating for external disturbance, while the LuGre friction model is responsible for friction compensation. In addition, the CCCMAC consisting of a cerebellar model articulation controller and a cross-coupled controller aims at reducing contour error and dealing with the problem of dynamics mismatch between different axes. Performance comparisons between the proposed fuzzy logic-based Reinforcement Iterative Learning Cross-Coupled Cerebellar Model Articulation Controller (RIL–CCCMAC) and several existing control schemes are conducted on a bi-axial motion stage. Experimental results verify the effectiveness of the proposed RIL–CCCMAC
Development of a modular control algorithm for high precision positioning systems
Ankara : The Department of Mechanical Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical references.In the last decade, micro/nano-technology has been improved significantly.
Micro/nano-technology related products started to be used in consumer market
in addition to their applications in the science and technology world. These
developments resulted in a growing interest for high precision positioning systems
since precision positioning is crucial for micro/nano-technology related applications.
With the rise of more complex and advanced applications requiring smaller
parts and higher precision performance, demand for new control techniques that
can meet these expectations is increased.
The goal of this work is developing a new control technique that can meet
increased expectations of precision positioning systems. For this purpose, control
of a modular multi-axis positioning system is studied in this thesis. The multiaxis
precision positioning system is constructed by assembling modular single-axis
stages. Therefore, a single-axis stage can be used in several configurations. Model
parameters of a single-axis stage change depending on which axis it is used for.
For this purpose, an iterative learning controller is designed to improve tracking
performance of a modular single-axis stage to help modular sliders adapting to
repeated disturbances and nonlinearities of the axis they are used for. When
modular single-axis stages are assembled to form multi-axis systems, the interaction
between the axes should be considered to operate stages simultaneously. In
order to compensate for these interactions, a multi input multi output (MIMO)
controller can be used such as cross-coupled controller (CCC). Cross-coupled controller
examines the effects between axes by controlling the contour error resulting
in an improved contour tracking.
In this thesis, a controller featuring cross-coupled control and iterative learning
control schemes is presented to improve contour and tracking accuracy at the
same time. Instead of using the standard contour estimation technique proposed
with the variable gain cross-coupled control, presented control design incorporates
a computationally efficient contour estimation technique. In addition to that,
implemented contour estimation technique makes the presented control scheme
more suitable for arbitrary nonlinear contours and multi-axis systems. Also, using
the zero-phase filtering based iterative learning control results in a practical design
and an increased applicability to modular systems. Stability and convergence of
the proposed controller has been shown with the necessary theoretical analysis.
Effectiveness of the control design is verified with simulations and experiments
on two-axis and three-axis positioning systems. The resulting controller is shown
to achieve nanometer level contouring and tracking performance.Ulu, Nurcan GeçerM.S
PSO based feedrate optimization with contour error constraints for NURBS toolpaths
Paper presented at MMAR 2016 conference (Międzyzdroje, Poland, 29 Aug.-1 Sept. 2016)Generation of a time-optimal feedrate profile for CNC machines has received significant attention in recent years. Most methods focus on achieving maximum allowable feedrate with constrained axial acceleration and jerk without considering manufacturing precision. Manufacturing precision is often defined as contour error which is the distance between desired and actual toolpaths. This paper presents a method of determining the maximum feedrate for NURBS toolpaths while constraining velocity, acceleration, jerk and contour error. Contour error is predicted during optimization by using an artificial neural-network. Optimization is performed by Particle Swarm Optimization with Augmented Lagrangian constraint handling technique. Results of a time-optimal feedrate profile generated for an example toolpath are presented to illustrate the capabilities of the proposed method
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